A hybrid recommender system to improve circular economy in industrial symbiotic networks

energies Article A Hybrid Recommender System to Improve Circular Economy in Industrial Symbiotic Networks Anna Gatzioura 1,2 , Miquel Sànchez-Marrè1,2,* and Karina Gibert 1,3 1Knowledge Engineering & Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Centre (KEMLG-@-IDEAI), Universitat Polit è cnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain; [email protected] (A.G.); [email protected] (K.G.) 2Department of Computer Science, Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain 3Department of Statistics and Operations Research, Universitat Politècnica de Catalunya BarcelonaTech (UPC), Catalonia, 08034 Barcelona, Spain *Correspondence: [email protected] Received: 10 August 2019; Accepted: 10 September 2019; Published: 16 September 2019   Abstract: Recently, the need of improved resource trading has arisen due to resource limitations and energy optimization problems. Various platforms supporting resource exchange and waste reuse in industrial symbiotic networks are being developed. However, the actors participating in these networks still mainly act based on predefined patterns, without taking the possible alternatives into account, usually due to the difficulty of properly evaluating them. Therefore, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find resources able to cover their needs, is still of high importance both for the companies and the whole ecosystem. In this work, we present a hybrid recommender system to support users in properly identifying the symbiotic relationships that might provide them an improved performance. This recommender combines a graph-based model for resource similarities, while it follows the basic case-based reasoning processes to generate resource recommendations. Several criteria, apart from resource similarity, are taken into account to generate, each time, the list of the most suitable solutions. As highlighted through a use case scenario, the proposed system could play a key role in the emerging industrial symbiotic platforms, as the majority of them still do not incorporate automatic decision support mechanisms. Keywords: hybrid recommender systems; industrial symbiotic networks; case-based reasoning; waste optimization; energy consumption optimization 1. Introduction Intelligent Decision Support Systems (IDSS), and especially Recommender Systems (RSs), are being used in the majority of current application domains, where users come across a lot of alternatives and information that have to discover, process, and use. RSs have been identified by Ricci et al. [ 1 ] as among the most promising techniques that are able to handle the current information overload and support users in their decision making processes. In recent years, these techniques have gained ground and their use has been extended to various application domains facing information overload problems. Industrial Symbiotic Networks (ISN) form an example of such domain, as the actors participating in these networks usually act based on predefined patterns due to the difficulty of properly identifying all possible symbiotic alternatives. Industrial symbiosis (IS) has been defined by Chertow [ 2 ], as “engaging traditionally separate industries in a collective approach to competitive advantage involving physical exchange of materials, energy, water, and by-products”. Furthermore, ISN have proven to be Energies 2019,12, 3546; doi:10.3390/en12183546 www.mdpi.com/journal/energies Energies 2019,12, 3546 2 of 24 successful in waste treatment and re-use as well as in creating new business opportunities. Therefore, the recommendation of effective possible synergies, being “matches between companies interested in providing and using their waste”, that the users were not aware of, would lead to both economic and environmental benefits for the companies and the sector as a whole. Therefore, the ability of a platform to deliver such recommendations is crucial, as companies many times are not willing to change their established workflows if there are not sure about the outcome. This work forms the extension of our previous work presented in [ 3 ]. In this paper, we describe, in detail, our hybrid RS [4] initially designed and developed for the needs of the SHAREBOX project. The aim of this system is to support industrial users in identifying possible resources and symbiotic partners of which they were not aware, and may provide them with improved performance. The implemented recommender first uses a graph-based similarity model to identify resource similarities that are based on their European Waste Codes (EWC). Subsequently, it performs a hybrid case-based recommendation to find the solutions best matching the user queries. Various criteria, like the user resource needs, the current situation of the system, as well as proper resource characteristics, are taken into account in order to generate the list of the most suitable solutions for a user at a given moment. Although it has been implemented for a specific platform, this system could be easily adapted to be used as a complementary tool in similar Industrial Symbiosis IDSS. The structure of the paper is as follows: in the next section, the theoretical background related to energy efficiency and Industrial Symbiotic Networks, as well as Recommenders Systems and Case-based Reasoning, being the core methodology used in our recommender, can be found. Following, in Section 3, the developed system is presented and, in Section 4, a case study highlighting its use in an Industrial Ecosystem can be found. Finally, in Section 5, we discuss the results presented and their importance towards energy consumption optimization, while in Section 6, we conclude and present the basic points of our future work. 2. Background 2.1. Industrial Symbiosis and Sustainability During the 20th century, the average temperature of the earth has increased by 0.6 degrees, and it is estimated that it will increase 1–5 more degrees in the next century [ 5 ]. Green House Gas emissions (GHG) and solid waste management are major causes of it and they require urgent measures to reduce current trends [ 6 ]. The first one, mainly related with fossil fuels combustion, is related with both transportation and industrial emissions [ 5 ]. Apart from the obvious environmental impact of human activities, air and soil pollutants have, in turn, impact in health [ 6 ]. The need to find sustainable resources that assist initiatives to reduce global warming and protect the environment, and indirectly human health, is paramount. The change of paradigm in energy towards renewable sources [ 7 ] and the reduction of waste emissions and primary resource use in resource intensive industries is suggested as one of the critical pathways to accelerate sustainable development [8]. Process integration emerged as a powerful engineering-oriented methodology in the 1970s to achieve substantial reductions in industrial energy, water, and utility use [ 9 ], and led to numerous design methods and industrial applications [ 10 ]. PI is a holistic approach for engineering design that aims to minimize resource and energy consumption. It takes a bottom-up perspective that is based on thermodynamics. The core principle of PI is to consider a system holistically; individual components are integrated to achieve synergy for the benefit of the whole system [ 11 ]. In [ 12 ], an optimization tool that is based on evolutionary algorithms is proposed to optimize the process gas network in an integrated steel plant, where cost and CO 2 emissions are simultaneously minimized. In [ 13 ], a process is proposed to generate low cost activated carbon from natural resources, like nuts shells, to eliminate heavy metals from industrial wastewater. In Europe, some directives promote the use of biofuels [ 14 ] and renewable energies [ 15 ] to reduce GHG emissions. In [ 6 ], recycling was proposed as one the main ways of reducing waste. Net GHG Energies 2019,12, 3546 3 of 24 savings from paper recycling and composting were quantified between 50 and 280 Kg CO 2 eq/ton MSW. For other materials (glass, plastic, ferrous metals, textile, and aluminum), recycling offers an overall GHG flux saving between 30 and 95 Kg CO2eq/ton MSW [16]. European Council Resolution of 24 February 1997 on a Community strategy for waste management confirms that waste prevention should be the first priority of waste management, and re-use and material recycling should be preferred to energy recovery from waste, where and insofar as they are the best ecological options. A number of European Directives, resolutions, and decisions of European Council between 1999 and 2006 establish legislation and recommendations for waste management and for reducing waste emissions at specific aspects, like periods of waste storage before disposal to reduce waste going to landfills [ 17 ], end of life vehicles [ 18 ], electrical equipment [ 19 ], batteries [ 20 ], etc. Directive 2006/12/EC on waste provides a general framework that requires waste management plans for each establishment. European Directive 2008/98/EC on waste is an improvement of D/2006/12/EC were protection of environment and human health comes into the priority and moves from waste management to reduction of waste emissions encouraging member states to promote re-use and recycling of industrial by products for sustainability reasons. In this context, circular economy and circular ecology, in particular, becomes a priority. The term circular economy was introduced for the first time in an occidental context in 1980 [ 21 ] to describe interactions between the economy and environment in a closed framework. In Germany and Japan, circular economy is based on the management of waste through the 3R (reduce, recycle, reuse), transforming the traditional productive cycle (resource-product-waste) into a circular flow (resource-product-recycled resource), thus reducing resources and waste, in line with industrial ecology field [ 22 ]. In 2012, the Ellen MacArthur Foundation report highlighted the economic and business opportunity of a circular restorative model and detailed the significant potential benefits for the European Union announcing cost savings of net materials, by 2025, worth up to $630 billion [ 23 ]. In 2015, EC adopted a Circular Economy Action Plan (COM 2015 (614)) [ 24 ] promoting circularity as a development backbone for a sustainable Europe. Very recently, in March 2019, the 54 actions under the action plan have been completed or are being implemented [ 25 ]. The plan establishes a concrete and ambitious program of action, with measures that cover the whole cycle: from production and consumption to waste management and the market for secondary raw materials and a revised legislative proposal on waste. Circular economy is interested in creating a circular flow of materials for a more efficient economy with fewer pollutants and other unwanted by-products, and industrial ecology, as popularized in [ 22 ], is the part of circular economy focusing on the waste and by-products management recycling and reuse in the industry. More specifically, industrial Symbiosis [ 26 ] is a subset of industrial ecology that is based on the idea of sharing mutually profitable transactions, where the by-products of some processes can substitute resources in other processes, and constitutes an excellent opportunity to contribute to the European Circular Economy Action Plan. Industrial symbiosis targets the industrial ecology goals through actions between firms [ 27 ]. In 2012, [ 23 ] reviews the original concept from Chertow [ 27 ] by eliminating the requirement of geographical proximity between symbiotic partners and including the possibility of involving non-industrial partners into the loop. The work in [ 28 ] provides some guidelines for policy development that promotes industrial symbiosis. The European Union highlights Industrial symbiosis as a methodology that stimulates industries to become more sustainable and recommends the development of platforms that helps industries to identify possible successful synergies. In [ 29 ], a recent survey of information systems assisting in the identification of industrial symbiosis is presented. In [ 30 ], the impact of knowledge injection into the industrial symbiosis assistants is explored and the interest of using Artificial Intelligence, electronic institutions, and semantics [ 31 ] to discover new synergies and promote eco-innovation is discussed. The SHAREBOX project (http://sharebox-project.eu/) proposes an integrated intelligent assistant for industrial symbiosis, which is well aligned with the need of developing stakeholders platform aimed in EC circular economy action plan. Energies 2019,12, 3546 4 of 24 2.2. Recommender Systems Recommender Systems (RSs) are tools and techniques for information retrieval and filtering, used to suggest items to be used or consumed by a user. They interact with the user to provide meaningful, effective, and personalized recommendations of items that might be of interest to the user. RSs either generate a set of personalized recommendations/suggestions of items that are expected to be useful for a certain user or try to predict whether a specific item will be of interest to a user or not, based on his/her previous preferences and those that are observed on similar users. In their simplest form, the recommendation is provided in form of a list of ranked items. The term “item” refers to the entity that was recommended by each recommender (ex: products, songs, web pages, services, etc.), while user refers to the entity (a person or another system), which interacts with the RS through some specific interface, in order to receive the recommendations. RSs help users with no or limited personal experience and knowledge in a specific area and, therefore, provide recommendations for users with a lack of ability to evaluate or select among the offered items [1,32,33]. The rapidly evolving functionalities of the Web, along with the wide use of Internet and other networking services in recent years, enabled sharing an increasing amount of information about many items of various types. The amount of information available, and the easy possibilities of collecting these kind of information and access to it, enabled users in the advanced search and review of item characteristics. On the other hand, the resulting information overload has increased the complexity of encountering and properly handling the required and correct information. Thus, the need of developing an intelligent RS, which is able to effectively support users in handling the information overload and decreasing the complexity of decision-making is denoted in many areas. Through the proper data selection and analysis, RSs support users’ decision-making processes, augmenting their ability as well as the quality of their decisions by enabling them to find items that presumably they will like to use [34]. The main components/data important for the use of a RS, can be divided into [32]: • Background data: information/data required by the system before the instantiation of the recommendation process (may be related to users, items context as well as to the recommendation process itself). • Input data that is the information provided by the user to the system to receive recommendations. • The core recommendation component, the algorithm used that combines and processes the background and input data to produce meaningful recommendations aligned with the active user’s request. The functionality of this component heavily depends on the type and scope of the RSs, as well as the data that this system is asked to process. The problem to be solved, or the central development idea to be supported by a RS is that a user wants to find the items that are able to best support his needs and their use will maximize the utility that was observed by the user. Based on the hypothesis of user rationality, a rational user who is aware of the alternative item choices would always select the item/situation that maximizes his/her utility under certain circumstances. Therefore, the intention of a RS is to estimate the utility values of different items’ use through the scope of a specific user and suggest him/her the item(s) that are most likely to provide maximal utility, therefore being preferred by him/her [34]. The most widely used recommendation methodologies in commercial applications can be mainly divided into Collaborative Filtering techniques and Content-Based techniques. Collaborative Filtering (CF) is based on recommending the items among those liked by users (“neighbors”) similar to the active user. CF techniques have been identified among the most successful approaches for RSs and they have been successfully used in data mining cases as well as in e-commerce applications. Based on the hypothesis that users have a stable buying behavior along time, i.e., when two users rated some items similarly in the past, they will probably evaluate other items in the future in a similar way as well. User profiles’ are built of the items that have been highly rated by them, and the similarity of users’ tastes is deducted from their previous ratings. For a given request, these Energies 2019,12, 3546 5 of 24 techniques first identify the neighbors of the active user, based on their previous ratings, and then from the set of their highly rated items recommend those that the active user has not yet tried. Although widely used in commercial applications, collaborative RSs still have to overcome the scalability and cold-start problems that limit their performance [34,35]. On the other hand, Content-Based (CB) techniques recommend to users items that are similar to those that they have shown preference in the past. In these systems user profiles are built from the characteristics of the items that a user has rated highly, and the items that she/he has not tried yet are compared to them. The items with the higher estimated possibility of being liked by the active user, as deducted from his/her past preferences, are then recommended. As CB techniques rely on more specific information about users and items, the creation of appropriate user and item profiles becomes more crucial. A profile that accurately reflects user preferences increases the system’s recommendation effectiveness. This is something that has become of great importance in the recently evolved business strategies in e-commerce. These techniques can recommend new items that could cover users’ needs. However, as they always recommend items that are similar to those that a user has already used and liked, recommendations’ can be limited on diversity and they can be overspecialized as well [33,34]. Other recommendation techniques, like Knowledge-based (Case-based, Constraint-based), Utility-based, Rule-based, Demographic, Context-aware, semantically enhanced, and especially hybrid, being combinations of different techniques, are also considered, due to the extended use of RSs in various areas, along with the shortcomings that the two previously described methodologies come with [4]. 2.3. Case-Based Reasoning Case-Based Reasoning (CBR) is a problem solving paradigm that uses old experiences to solve new problems. It is based on the following sentence, also known as the CBR assumption, “Similar problems have similar solutions”. A Case-based reasoner solves new problems by adapting solutions that have been successfully used in the past to the new situation [36]. It is a computational metaphor of the reasoning by analogy process, so frequently used in human reasoning. Indeed, CBR is closely related to the human way of thinking, reasoning, and acting through everyday situations when facing new problems that have to be solved. In such cases, there may be observed the tendency of recalling similar cases that have been successfully solved in the past or to avoid repeating the errors performed. Therefore, CBR, as a method for building intelligent reasoning systems, seems to be more natural and constitutes a problem solving methodology that can be applied to many domains, as it does not require building an explicit domain model. In addition, as CBR reasoners derive their reasoning from complete cases, rather than decomposing them into rules, they have the ability to adapt and improve their problem solving performance along time, in comparison to rule-based techniques, that contain dependencies that cannot be easily understood, and may turn to be insufficient for some domains of applications, without the use of expert domain knowledge [37]. In CBR, a case denotes a problem situation in a wider term; it may be any problem that is defined by a user that does not necessarily refer to finding a concrete solution to an application. A case can be defined as a set of values of specific characteristics that occurred in a situation. Depending on the type and the characteristics of the situation that is modeled, a different case representation is used to capture these specific attributes that specify this case and their values. Often, cases may be described and stored as collections of attribute-value pairs that can be easily stored and retrieved through the CBR cycle. However, in more complex situations, it is useful to use a more complex description, like a hierarchical object-oriented representation of the cases, where cases are represented as a collection of structured objects, instances of a class, which enables their decomposition and analysis as well as the use of inheritance and the extraction of possible relations among the objects parts. In such an object-oriented approach each object represents a closed part of the situation, each belonging to a class and being described by a set of features. Finally, for special applications, a graph representation might Energies 2019,12, 3546 6 of 24 be adequate, where a case is represented as a set of nodes and arcs or in others predicate logic may be used in order to represent the cases as sets of atomic formulas [38,39]. The selection of the most appropriate cases’ representation in a problem depends on the problem domain and on the scope of the CBR system, as well as on the amount and structure of the already available data that form the case base. Depending on the complexity of the representation used, the complexity of the used similarity metric will also vary. 2.3.1. CBR Cycle The CBR solving and learning process can be described as a cyclical process comprising from 4 parts. The following four processes, also known as “the four REs” (Retrieve, Reuse, Revise, Retain), or as the CBR cycle are [36]: •Retrieve: the most relevant cases among those previously experienced from the case memory. • Reuse (or adapt): the information and knowledge provided by the by the retrieved case(s) in order to solve the new problem. •Revise (or evaluate): the solution obtained • Retain (or learn): the parts of the solution/experience that are likely to be used (reused or avoided) for future purposes and incorporate this new knowledge into the case base. The CBR cycle can also be seen in Figure 1, below, where as we can see except from the knowledge obtained by the cases in the case base there is also general, domain dependent knowledge present, supporting the CBR process. Each of the processes in the cycle is further divided into subtasks, depending on the type of the application domain. Figure 1. The Case-Based Reasoning (CBR) cycle. When a new problem (“new case”) asking for solution comes, the CBR approach begins with retrieving one or more of the previously experienced similar cases among those that were recorded in the case base. This step also requires an indexing of the existing cases based on appropriate features, therefore similarity measures are involved in this step. The solution is obtained by reusing the most similar among the previous cases that were retrieved after adapting it, so that it becomes adequate for the new problem. The solution can either be modified manually by the users wishing to define some characteristics of interest to them, or automatically by the system based on domain knowledge and solution generators that are able to adapt the solution to the special requests. After being derived, the new problem solution is evaluated in order to ensure that it is adequate for the initial problem and to verify that its quality and performance will be the expected. Finally, the new experience is added into Energies 2019,12, 3546 7 of 24 the existing case base by providing additional solution knowledge or an explanation of a way that performed mistakes can be avoided in the future [39]. 2.3.2. The Similarity Concept in CBR A core concept of the CBR methodology and a key factor of its successful application is the similarity measure that is used for cases retrieval, i.e., the measure used to quantify the degree of resemblance between a pair of cases. The purpose of the use of similarity metric is to select those cases that are most similar to the new case from the case base. Therefore, they may have the same solution as the current problem or their solution can be easily adapted to match the characteristics of current problem. The similarity metric provides an apriori approximation of the rate of the utility the solution is going to provide to its reusability, with the intention to provide a good approximation that is as close to the real value of reusability as possible, while at the same moment being easily computable and interpretable. Cases can be represented in different ways, and several similarity metrics can be used for each case representation. The values of the similarity function normally range in the interval [0, 1], with 0 being assigned to totally different cases and 1 to cases that are regarded as identical through a concrete similarity measure [ 38 ]. Any normalized metrics adapted to the nature of the cases are suitable for a CBR system. There have been various similarity metrics proposed in literature [ 40 ]. Usually, the notion of a metric or distance d(x,y) between two objects, x and y, has been used in order to reflect the level of similarity or dissimilarity among the elements in a given set, where, through the relation S(x,y)= 1 −d(x,y) , distances d(x,y) can be transformed into proper similarity measures S(x,y) , provided that d(x,y) evaluates in the interval [0,1]. In order to build the CBR model, there is a need of retrieving the kmost similar cases. The k-nearest neighbors’ algorithm approach can be used to specify these cases. When considering a set of n features fi , i= 1, . . . , n used to describe the objects and evaluating in the space [0, 1] and being wi the importance weighting factor of a feature fi , the similarity between the input case (represented by ( fI 1 , . . . , fI n )) and a retrieved case from the case base (represented by ( fR 1 , . . . , fR n )) can be represented as the weighted average of the local similarities feature by feature (simfI i,fR i: Pn i=1wi×simfI i,fR i Pn i=1wi (1) Based on this, most case-based reasoners use a generalized weighted dissimilarity measure that can be defined as [41]: dfI,fR=Pn i=1wi×(fI i−dfI i,fR i) Pn i=1wi (2) where simfI i,fR i= ( fI i−dfI i,fR i ) and dfI i,fR i describe the dissimilarity level between the i-th feature of the compared cases. In cases that are characterized by symbolic or Boolean attributes, or by attributes of more complex types, different and often more complicated local similarity functions have to be defined, more than the distance measures. Commonly, the case description contains both numerical and qualitative information. In these cases, compatibility measures are appropriated [ 42 ]. When a semantic interpretation of the qualitative terms is also available, through some formal representation, like a reference ontology, semantic distances can be also used [ 43 ]. Additionally, the qualitative terms can be used as tags and co-occurrences can be exploited for the computation of case similarities. 2.4. Case-Based Recommenders Case-Based Recommenders have mainly emerged as an alternative to CF recommenders intending to overcome the shortcomings that CF recommenders come with, while efficiently handling the existing information overload problem. Energies 2019,12, 3546 8 of 24 The predominantly used CF recommendation techniques focus on users’ preferences, as these can be expressed through users’ purchase histories and item assigned ratings, without taking into account the general context in the moment of the item selection or the market situation at the transaction moment. The recommendations are derived from the items that users, similar to the active user, have selected in the past and evaluated positively, more than identifying and analyzing the attributes of the available items. Therefore, item ratings more than item attributes’ descriptions are required by this type of recommenders. CBR recommenders, on the other hand, will generate recommendations for an active user that are based on the analysis of the item characteristics and by trying to find the item(s) that best match a user request. These recommenders include in the recommendation generation process semantic ratings and characteristics, as these can be extracted from past item selection cases with similar user requirements, placing their emphasis on the description of the requirements and the characteristics of the cases. CBR recommendations are able to provide accurate results when applied to domains where the individual products are described in terms of a well-defined set of features [38]. Case-based recommenders implement a type of content-based recommendation that relies on a structured representation of items as a set of well-defined characteristics/features and their values, in contrast to general content-based recommenders that usually rely on unstructured or semi-structured items’ representations. These representations allow for case-based reasoners to make judgments about product similarities and based on those to provide recommendations, more than simply using the ratings that are assigned to products by various users. The existence of a structured and common way of representation of the treated items enables case-based recommenders in calculating and understanding the similarities among those items, the generation of meaningful item recommendations of high quality to the users while enabling the evaluation of the outcome in terms of user satisfaction and the incorporation of this feedback. Case-based recommenders rely on the core concepts of retrieval and similarity of CBR. For CBR recommender, the user query serves as a problem specification, while the item descriptions form the cases in the case base. The set of existing items is represented as cases of which the case base is made up, while the item(s) to be recommended to users are the items (cases) that are retrieved form the case base, based on their similarity to the user’s request as this is defined in the same space, the space of items’ characteristics. These characteristics depend on the type of the specific item traded in each situation. Depending on the type of the item to be recommended and its special characteristics, different local similarity measures are aggregated and modeled into a different global similarity function that is then used for the final item selection and highly affects the outcome of this process [44]. 3. System Description 3.1. The SHAREBOX Platform The main functionality of the SHAREBOX system is to allow a user, which usually will be an employee of one company, to specify the resources he/she WANTS or HAS, with the final aim of obtaining some possible synergies with other companies. Asynergy is a symbiotic relationship between two companies where one resource or waste produced by one company (i.e., a HAVE of the company) can be consumed by the other company, which needs it for productive activity (i.e., a WANT of the other company). The SHAREBOX platform lets the user introduce the characteristics of the resources WANTED/HAD, which are usually defined by some descriptive data, like EWCs codes, some keywords, etc., and for each selected resource WANTED/HAD, the user asks to the system for possible synergies with other companies. Currently, there are several modules implementing different approaches to calculate the suggested synergies. Some of these components, especially some input-out modelling techniques, could be very hard from a computational point of view, particularly when the resource database of the SHAREBOX platform will be highly populated. This seems to leave space for trying to improve the computational Energies 2019,12, 3546 9 of 24 time spent to obtain the suggestion of the synergies for a given company. On the other hand, there is another important feature that can be improved: the quality of the recommended synergies, because an intelligent component could learn from the past synergies performed through the platform system. This way, each time that a synergy is finally completed, a new valuable experience and information can be learnt by this intelligent module, issuing at improving future recommendations, and also learning which synergies were not successful, aiming at not making the similar errors in the future. Currently, the SHAREBOX architecture can be outlined, as depicted in Figure 2. Figure 2. Current SHAREBOX System Architecture. 3.2. Hybrid Recommender System Overview In Figure 3, the new version of the platform including the recommender is shown. Although the system was designed for the SHAREBOX platform it could be easily adapted to other platforms serving industrial symbiotic networks with similar needs. 3.3. Scope The scope of the presented recommender system is to generate the list of top-N recommendations in terms of possible synergies matching a given user request related to a specific resource and the current resource availability status. More specifically: •Given: # A list of company sites, with their industry codes, geographical locations, resource needs, and offers # A query that consists of, at least, the site location, the resource type, in terms of European Waste Code (EWC), category and the definition of whether it is a needed or offered waste • Generate the list of the N most adequate resource synergy candidates, those from the available matching in a locally optimal way the given query. Energies 2019,12, 3546 16 of 24 1. A user inserts a resource request, containing the specifications of the desired resource, in terms of key words, EWC codes and resource categories, the resource direction specifying whether it is a desired or an offered resource, and finally the search method mode, defining it apart from the exact matching descriptions other similar resources can be considered as candidates. In addition, the site geolocation is retrieved from the stored records. 2. A first filtering of the resources is performed, based on the request direction parameter that has been specified in the user query. If the performed query is about a want waste only the records related to have waste(s) will be evaluated. On the other hand, in the case of a have request only the want records will be taken into account. If the user is simply browsing to see what resources are available in the system and no direction is stated, all of the resources are retrieved at this stage. 3. The recommender will then identify the candidates that could address the user request, in terms of resource properties. By candidates, we refer to the various alternative resources with characteristics that could serve a user request. First, if a user has specified a filtering mode at EWC or category level, first the available resources are filtered based on this parameter. Subsequently, this new set of resources is used for further evaluation. 4. Finally, the core recommendation mechanism, with scope to return the ordered list containing the closest matchings of the requested waste, is based on a similarity/distance function comparing the user’s request(s) and the existing cases’ parameters. Let qbe the user request and rone of the existing resources after the filtering processes. The locations of the companies as these have been entered to the platform, the available and requested quantities along with their degree of similarity are among the parameters that are used in the distance function. More specifically, the total semantic distance among qand rwill be a function of the following general form: d(q,r)=fsimresq,resr,simlocq,locr (6) where: •simresq,resr is the similarity function that is used to calculate the semantic distance of the requested and the candidate resources, given that not only exact matches (the same resource) are considered as acceptable solutions, but also closely similar resources may be used. This function is a combination of the similarity functions (5) described previously and the terms described in Sections 3.5.3 and 3.5.4. •simlocq,locr is a function that evaluates the geographical distance between the requesting and the candidate sites’ locations. Usually, the resource candidates would be directly searched by the EWC(s) submitted by the user, and only exact matches (if any) would be presented as possible solutions. In order to provide additional possibilities to the users, this Recommender first identifies the associated, most similar with the requested resource code, additional codes. Under these codes items with same properties or closely similar can be found, although being classified under different codes. Due to the EWC categorization, which is based on the processes and the industrial sectors from which waste comes, and not on its recourse properties, many times waste with identical properties is classified under different codes. Thus, an important functionality of the system, which is able to deliver an added value to the platform’s users, is the identification of those alternative resource candidates. 4. Case Study In this section, the presentation of an industrial symbiotic ecosystem, being the application domain of our RS, can be found. In addition, there is the presentation of recommendations related to the resources that are involved in synergies with higher frequency in this ecosystem. In industrial symbiotic networks there is a clear terminology regarding the different inputs needed and the different outputs generated by the industries. The primary inputs are those inputs that are Energies 2019,12, 3546 17 of 24 not produced by any industry, such as raw materials and energy. The primary outputs are the outputs resulting from a manufacturing process that is the primary focus of production. Secondary output generally refers to waste that has no perceived economic value and that traditionally are to be discarded. Some concepts are important, like the substitution rate of a primary input A (obtained from waste recycling) over primary input B, in a given production process, is referred as a/b if a units of A can be used instead of b units of B in the production process. The recycling rate of primary input C over waste D in a specific recycling process is referred as c/d if by means of recycling d units of D, we can obtain c units of C in the recycling process. Usually, industries in a country are located in some industrial parks, where several industries are not far located in the same industrial zone, and usually they share some characteristic features, like, for instance, producing several products that can be used by a determined type of industry, like a ceramic industrial park, were several industries produce tiles, other produce some components that will be used by the tile industries as primary inputs, like some glass powder, others produce some mineral components, etc. Some real examples of industrial parks are: • Industrial Cluster (La Mina Industrial Zone and Castell ó Harbour) in Nules, Castell ó de la Plana, Spain •Industrie Center Obernburg, in nearby Frankfurt, Germany •Uslan Eco-Industrial Park (EIP), South Korea •TaigaNova Eco-Industrial Park in Fort McMurray, AB, Canada •CleanTech Park, Singapore 4.1. Industrial Ecosystem As a case study to analyze how the designed Recommender system works, an industrial ecosystem will be described. Let us suppose that we have a wide industrial park outside a great city with the following eight industries located in the park: 1. CeramProd, which is an industry producing ceramic manufacturing products, like Floor-Tile-Ceramics, Wall-Tile-Ceramics, and Roof-Tile-Ceramics, and it needs Frites, Recycled-Water, and Pigments. 2. NewFarm, which is a modern industry producing several outputs of a farm like Feathers, Leather-cuttings, Milk, and needs Wooden-Packaging and Glass-Bottles. 3. StoneRock, which is an industry producing several materials, like Chrome, Paint-Powders, Frites, and needs Mechanical-Equipment and Sand. 4. SweetHome, which is a modern industry producing several home items like Pillows, Bed-Sheets, Table-Clothes, and needs Feathers, Silk-Waste, and Wooden-Packaging. 5. HotelNew, which a hotel inside the industrial park which has Wooden-Packaging, produces Domestic-Wastewater and needs Pillows, Bed-Sheets, and Drinking-Water. 6. ConsMat, which is a modern industry producing materials for the construction, and mainly produces Bricks, and needs Sludge, Pigments, Paint-Powders, and Recycled-Water. 7. DrinkWater is a modern WasteWater Treament Plant including a tertiary treatment devoted to the production of DrinkingWater and also generates Sludge as a subproduct, and it needs Industrial-Wastewater, Domestic-Wastewater, and punctually it needs Steel-Pipes for some maintenance operations. 8. ChefRest, is a restaurant within the industrial park which produces Domestic-Wastewater, and needs Milk, Drinking-Water, Table-Clothes, and Textile-Packaging. Their needs and outputs are described in Table 1. Energies 2019,12, 3546 18 of 24 Table 1. Description of the industries needs and output products within the industrial park. Company Product H/W Quant CeramProd Floor-Tile-Ceramics Have 2000 CeramProd Wall-Tile-Ceramics Have 8000 CeramProd Roof-Tile-Ceramics Have 1000 CeramProd Frites Want 5700 CeramProd Recycled-Water Want 15,000 CeramProd Pigments Want 3750 NewFarm Feathers Have 2 NewFarm Leather-cuttings Have 50 NewFarm Milk Have 3000 NewFarm Wooden-Packaging Want 1500 NewFarm Glass-Bottles Want 2500 StoneRock Chrome Have 4150 StoneRock Pigments Have 6000 StoneRock Paint-Powders Have 1000 StoneRock Mechanical-Equipment Want 4000 StoneRock Sand Want 8000 StoneRock Frites Have 6100 SweetHome Feathers Want 5 SweetHome Silk-Waste Want 2500 SweetHome Wooden-Packaging Want 1000 SweetHome Pillows Have 200 SweetHome Bed-Sheets Have 400 SweetHome Table-Clothes Have 300 HotelNew Pillows Want 100 HotelNew Bed-Sheets Want 50 HotelNew Wooden-Packaging Have 1000 HotelNew Cardboard-Packaging Have 1000 HotelNew Drinking-Water Want 20,000 HotelNew Domestic-Wastewater Have 6000 ConsMat Sludge Want 20,000 ConsMat Bricks Have 250 ConsMat Pigments Want 3000 ConsMat Paint-Powders Want 250 ConsMat Recycled-Water Want 5000 DrinkWater Drinking-Water Have 25,000 DrinkWater Industrial-Wastewater Want 35,000 DrinkWater Domestic-Wastewater Want 4000 DrinkWater Sludge Have 8000 DrinkWater Steel-Pipes Want 6000 ChefRest Milk Want 1000 Energies 2019,12, 3546 19 of 24 Table 1. Cont. Company Product H/W Quant ChefRest Drinking-Water Want 5000 ChefRest Domestic-Wastewater Have 2500 ChefRest Table-Clothes Want 30 ChefRest Plastic-Packaging Want 2000 ChefRest Wooden Pallets Have 2000 Let us suppose that the company ChefRest, which has recently installed in the industrial park, currently needs 1100 L of Milk, 5200 L of Drinking-Water and 45 Table-Clothes. Usually, it had some supplier for the milk and the table clothes, which was very far from its location (approximately 200 km). In addition, the drinking water was supplied for a municipal company in its previous location, but the prix of water was expensive 5 €/L. Of course, the idea, is that now, ChefRest, if they knew that in the same industrial park there are some other industries that produce the primary outputs they need, they will optimize the price they were paying, the transportation costs and the corresponding energy savings, and even they could take the Drinking-Water, from the nearby DrinkWater company, which is a Drinking Water Plant, which reduces the consumption of water and its equivalence in energy costs. All of these new resources, hidden until now for a user, can be made apparent to a user of the SHAREBOX platform, and the new wastes, which will be used as primary resources will be suggested by the recommender system integrated in the platform. In the next section, the recommendations of our system will be detailed to outline the benefits of the industrial symbiotic networks, and the possible synergies that can be discovered or maintained in time to save costs, energy, and primary outputs through recycled waste materials. 4.2. Resource Recommendations’ Examples Based on waste needs and offers of our testing ecosystem, as described in Table 1, we describe two possible resolution scenarios, following the steps previous described. 4.2.1. “Wanting Wooden Packaging” Resolution Workflow Supposing a query stated by the SweetHome administrative user, looking for wooden packaging, described as: •Keyword(s): packaging •Direction: Want •EWC: 15 01 03 #Search method: Filter •Category: null •Site: SweetHome geolocation The algorithm first evaluates the direction of the query and the registered available resources in the system are filtered based on this attribute, returning only the resources of the opposite direction. In this case, it is a “want” resource. Therefore, the records of sites that “have” resources are retrieved and further evaluated. As a second step, the similarity of the retrieved resources with the queried one is evaluated. In this example, the search method is set to “filter” therefore only resources with EWCs exactly matching the query EWC could be used. As here no resource category has been selected, this attribute is not being evaluated. From the existing resources, we see that the only “have” resources with the same EWC are Energies 2019,12, 3546 20 of 24 the wooden packaging offered by HotelNew and the wooden pallets that ChefRest has. For instance, the cardboard packaging offered by HotelNew has a EWC equal to 15 01 01 [ 48 ]. From those two candidates we now evaluate the linguistic descriptions written in the keywords text. As the query has been for “packaging” a 50% of the words in the resource’s description of the HotelNew, i.e., wooden packaging, is matching the query, while the wooden pallets do not. Therefore the recommended resource are the wooden packaging offered by HotelNew. If this recommendation is accepted and a synergy is established between the two parties, the conditions (like final quantity, price, date, etc.) can be negotiated between the two parties and the synergy included into the synergies case base for being considered in future. The added value of the recommender is to refine all possible packaging alternatives in the system that would be listed if the recommender would not be in place, to just suggesting the ones that maximize the resources that the user really is looking for. 4.2.2. “Wanting Plastic Packaging” Resolution Workflow Supposing a query stated by the ChefRest administrative user, looking for plastic packaging, described as: •Keyword(s): packaging •Direction: Want •EWC: 15 01 02 #Search method: Rank •Category: null •Site: ChefRest geolocation In this case, the algorithm again retrieves only the records of sites that “have” resources. As the search method is set to “rank” not only resources with EWCs exactly matching the query EWC are taken into account. From the existing resources, tagged as “have”, there is none with exactly the same EWC. However there exist different types of packaging, like the cardboard packaging and wooden packaging, both offered by HotelNew. For instance, the cardboard packaging has EWC equal to 15 01 01 and the wooden packaging 15 01 03 [ 48 ]. As calculated using Equation (5), both of these EWCs have a similarity degree of approximately 0.45, while both linguistic descriptions match the query keyword at a 0.5 degree of similarity. Finally, the sites’ distance between the query and the provider sites is calculated. Both candidates will be presented to the user, with the nearer one being presented first. In this case, the final decision relies on the user, as no resource exactly matches the query or there is no resource that significantly outperforms the other that can be automatically identified. 5. Discussion The two case studies presented illustrate major use of the recommender system and its advantages. The first case study shown the recommender filtering mode. In that mode, the resources recommended are filtered to have the same EWC than the one specified in the query. This behavior is the usual in Information Systems querying a database of resources. Main problem is that this querying systems are based on an exact-matching method to recover possible resources. The results on this kind of systems is that the user needs to know perfectly the resource is searching for, and the system only will provide with the answer of the resource searched with the same EWC (like wooden packaging and wooden pallets in the case study), if it is available according to the database of resources, or will provide an empty answer if there is no available with the exactly same EWC. This is a great drawback. There could be similar waste materials, which, even though they do not have the same queried EWC, are really similar to the resource searched in the query (like cardboard packaging in the case study). Thus, usual Information Systems will never get the other possible candidate resources. Furthermore, the recommender system used in filter mode has an advantage to classic database querying systems. In addition to filtering the Energies 2019,12, 3546 21 of 24 possible resources by the exact EWC, as it applies a similarity measure computation based on the linguistic description of the resources and the keywords enumerated in the query, the recommender can order and give a priority value to each possible candidate (with the same EWC), like in the case study, where the system gives more weight to the wooden packaging resource than the wooden pallets, because the wooden packaging is more similar to the searched resource because share the packaging word in the description of the resource. This means that the recommender, even filtering resources give a more useful list of suggestions than usual Information Systems offering a similar management of resources for setting industrial symbiosis synergies. The recommender system shows its full potential and great advantage over traditional Information Systems, on the situations that are illustrated by the second case study. When the recommender is used in rank mode. In the recommender ranking mode the resources recommended are finally ranked, according to a similarity measure, which takes into account the degree of similarity between the EWCs, the similarity between the keywords in the query and the linguistic description of the resources, the distance between the location of the resource, and the site of the user, etc. Thus, it is using a similarity-matching method. This way, like in the second case study, all of the candidate resources are ordered according to the similarity measure (like cardboard packaging and wooden packaging), and the most suitable ones are on the first positions of the list. This way, the possibility to find the most useful resource is maximized. This is a great advantage against Information Systems based on the exact-matching approach offered by a database querying system. In addition, given a concrete query, the recommender is able to make aware to the user of other unknown possible resources partially matching the characteristics of the resource queried by the user, like the cardboard packaging resource in the first case study, if a ranking mode would have been used. The great value that is added by this hybrid recommender system is that it provides the SHAREBOX platform with a very flexible recommendation system for suggesting the most suitable resources, given a query of a user, and on the other hand, provides it with a resource discovering functionality, with its hybrid recommendation strategy. As a consequence, the reusing of waste products, the reductions on new raw material for productive industrial processes, and at the same time, the energy cost savings that are derived on the established synergies are of great value. The proposed synergies open the opportunity that the user introduces a by-product as an input to their processes, with the corresponding economic, energetic, and environmental savings, provided that the by-product will not need waste management treatments and the user will save all the costs of producing the corresponding primary resource. In the particular case that is presented in the paper, where all involved actors in the synergy are located in the same industrial park, there are, in addition, savings on transportation costs. In the midterm, it is expected that the feedback accumulated by the global synergy recommender will make the platform able to suggest unexpected synergies as well that the user a priori could not even be conceive and would never formalize in a query by himself or herself, thus contributing to the ecological innovation in industry. 6. Conclusions and Future Work This paper describes an intelligent hybrid recommender system that is included in the SHAREBOX platform, which is able to identify relevant new by-products that might match the needs of a firm and can substitute primary resources. The main contribution of the paper is that the system is based on the integration of the Case-Based Reasoning approach and Ontological knowledge from the EWCs. The recommender system uses the EWC codes as a standard resources ontology that permits identifying semantic proximity between resources and improving the system performance in general. Specific similarity functions are defined that exploit the structure of the EWC ontology, thus including the semantics of the EWC codes themselves in the similarity computation. In addition, other features are taken into account to get the similarity between the textual descriptions of the resources/by-products and the keywords that were provided by the end user in the queries. As an additional innovation, the similarity between the user query and the cases in the Case Base are enriched with information Energies 2019,12, 3546 22 of 24 regarding a second classification of wastes, which provides an alternative ontology of waste categories and the geolocalization of the two involved firms to prioritize synergies between closer companies, thus increasing the feasibility of the synergy. All these features provide the hybrid recommender system with a great flexibility, which enables and multiplies the probabilities to find the best resource/by-product options among the ones stored in the resource databases. This flexibility is a great advantage to conventional Information Systems, which offer the user exact-matching queries to the databases. The assessment of the recommender system in the SHAREBOX platform has been satisfactory. Nonetheless, further evaluation and validation tests must be performed to confirm the very good preliminary evaluation of the system. On the other side, a second level of recommendation can rely on the historical successful synergies that were performed in the SHAREBOX platform. This other synergy recommender system will increase performance as far as the platform accumulates new experience. The global synergy recommender is currently being integrated in the global platform and it can learn in the long term from the feedback regarding the success of the recommendations provided. This other component will make the platform more competent along time, because it will learn to recommend more synergies, and in a more accurate way. Author Contributions: All authors contributed equally to this work. Funding: This research was funded by [SHAREBOX (Secure Management Platform for Shared Process Resources) European project] grant number [H2020-SPIRE-2015-680843], and by [Catalan Agency for Management of University and Research Grants (AGAUR)] grant number [2017 SGR 574]. 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